It is an important direction to detect outliers efficiently from the incremental data. The method can be widely applied in auditing, stream media analysis etc. Since the network-based auditing system can extract data from the audited enterprise or government in real time, it is crucial to discover outliers efficiently as conditates of audit doubts from the incremental data. However, most of the outlier analysis algorithms are designed to handle the static data sets. The paper presents a density-based local outlier analysis algorithm which is based on the clustering result of historical data and can efficiently detect outliers from the incremental data. The algorithm is applied to audit the real social security data. The experiments show the effectiveness and the efficiency of the proposed method.